Other cell interference estimation

ABSTRACT

Mobile broadband traffic has been exploding in wireless networks resulting in an increase of interferences and reduced operator control. Networks are also becoming more heterogeneous putting additional demand in interference management. Scheduler schedules uplink transmissions from UEs based on a load prediction algorithm that typically assumes worst case. However, UEs do not always use full power granted, and thus, much of granted radio resources are wasted. To address these and other issues, technique(s) to accurately predict/estimate other cell interferences and thermal noise separately and to accurately predict/estimate load utilization probability and variance is(are) described. Inventive estimation technique(s) can be used to schedule UEs to more fully utilize available radio resources. Extended Kalman filtering can be adapted for use in estimation providing low order computational complexity.

TECHNICAL FIELD

The technical field of the present disclosure generally relates toestimating other cell interferences in a wireless network. Inparticular, the technical field relates to apparatus(es), method(s),and/or system(s) for estimating other cell interferences using loadutilization measurements.

BACKGROUND

Recently, at least the following trends have emerged in field ofcellular telephony. First, mobile broadband traffic has been explodingin wireless networks such as WCDMA (wideband code division multipleaccess). The technical consequence is a corresponding steep increase ofthe interference in these networks, or equivalently, a steep increase ofthe load. This makes it important to exploit the load headroom that isleft in the most efficient way.

Second, cellular networks are becoming more heterogeneous, with macroRBSs (radio base station) being supported by micro and pico RBSs attraffic hot spots. Furthermore, home base stations (e.g., femto RBSs)are emerging in many networks. This trend puts increasing demands oninter-cell interference management.

Third, the consequence of the above is a large increase of the number ofnetwork nodes in cellular networks, together with a reduced operatorcontrol. There is therefore a strong desire to introduce moreself-organizing network (SON) functionality. Such functionality maysupport interference management by automatic interference thresholdsetting and adaptation, for a subset of the nodes of the cellularnetwork.

As a result, there are problems that can hinder providing efficientservice. In WCDMA for example, the UEs (user equipments) may or may notutilize the power granted by the EUL (enhanced uplink) scheduler. Thisleads to an inaccuracy of the load prediction step, where the schedulerbases its scheduling decision on a prediction of the resulting airinterface load of the traffic it schedules. This is so since the 3GPPstandard has an inherent delay of about at least 5 TTIs (transmissiontime interval) from the scheduling decision until the interference powerappears over the air interface. Also the WCDMA load prediction does notaccount for all imperfections in the modeling of an UL (uplink) radioreceiver. This can lead to additional inaccuracies in the loadprediction and estimation steps.

The inventors are not aware of any practical other cell interferenceestimation algorithm available that can provide other cell interferenceestimates with an inaccuracy better than 10-20%, and does so with closeto transmission time interval (TTI, typically 2 ms or 10 ms) bandwidth(typically 250 or 50 Hz) over interested power and load ranges. As aresult, it is not possible to make optimal scheduling decisions sincethe exact origin of the interference power in the UL is unknown.

Load Estimation without Other Cell Interference Estimation

Following is a discussion on measurement and estimation techniques tomeasure instantaneous total load on the uplink air interface given in acell of a WCDMA system. In general, a load at the antenna connector isgiven by noise rise, also referred to as rise over thermal, RoT(t),defined by:

$\begin{matrix}{{{{RoT}(t)} = \frac{P_{R\; T\; W\; P}(t)}{P_{N}(t)}},} & (1)\end{matrix}$

where P_(N)(t) is the thermal noise level as measured at the antennaconnector. For the purposes of discussion, P_(RTWP)(t) may be viewed asthe total wideband power defined by:

$\begin{matrix}{{{P_{RTWP}(t)} = {{\sum\limits_{i = 1}^{I}\; {P_{i}(t)}} + {P_{other}(t)} + {P_{N}(t)}}},} & (2)\end{matrix}$

also measured at the antenna connector. The total wideband powerP_(RTWP)(t) is unaffected by any de-spreading applied. In (2),P_(other)(t) represents the power as received from one or more cells ofthe WCDMA system other than an own cell. The P_(i)(t) are the powers ofthe individual users. One major difficulty of any RoT estimationtechnique is in the inherent inability to separate the thermal noiseP_(N)(t) from the interference P_(other)(t) from other cells.

Another specific problem that needs to be addressed is that the signalreference points are, by definition, at the antenna connectors. Themeasurements are however obtained after the analog signal conditioningchain, in the digital receiver. The analog signal conditioning chainintroduces a scale factor error of about 1 dB (1-sigma) that isdifficult to compensate for. Fortunately, all powers of (2) are equallyaffected by the scale factor error so when (1) is calculated, the scalefactor error is cancelled as follows:

$\begin{matrix}\begin{matrix}{{R\; o\; {T^{{Digital}\mspace{14mu} {Receiver}}(t)}} = \frac{P_{RTWP}^{{Digital}\mspace{14mu} {Receiver}}(t)}{P_{N}^{{Digital}\mspace{14mu} {Receiver}}(t)}} \\{= \frac{{\gamma (t)}{P_{RTWP}^{Antenna}(t)}}{{\gamma (t)}{P_{N}^{Antenna}(t)}}} \\{= {R\; o\; {{T^{Antenna}(t)}.}}}\end{matrix} & (3)\end{matrix}$

To understand the fundamental problem of interferences from other cellswhen performing load estimation, note that:

P _(other)(t)+P _(N)(t)=E[P _(other)(t)]+E[P _(N)(t)]+ΔP _(other)(t)+ΔP_(N)(t).  (4)

where E[ ] denotes a mathematical expectation and where Δ denotes avariation around the mean. The fundamental problem can now be clearlyseen. Since there are no measurements available in the RBS that arerelated to the other cell interference, a linear filtering operation canat best estimate the sum E[P_(other)(t)]+E[P_(N)(t)]. This estimatecannot be used to deduce the value of E[P_(N)(t)]. The situation is thesame as when the sum of two numbers is available. Then there is no wayto figure out the individual values of E[P_(other)(t)] and E[P_(N)(t)].It has also been formally proved that the thermal noise power floor isnot mathematically observable in case there is a non-zero mean othercell interference present in the uplink (UL).

FIG. 1 illustrates a conventional algorithm that estimates a noisefloor. The illustrated algorithm is referred to as a sliding windowalgorithm, and estimates the RoT as given by equation (1). The mainproblem solved by this conventional estimation algorithm is that it canprovide an accurate estimation of the thermal noise floor N(t). Since itis not possible to obtain exact estimates of this quantity due to theother cell interference, the estimator therefore applies anapproximation, by consideration of a soft minimum as computed over arelative long window in time. It is important to understand that thisestimation relies on the fact that the noise floor is constant over verylong periods of time (disregarding the small temperature drift).

One significant disadvantage of the sliding window algorithm is that thealgorithm requires a large amount of storage memory. This becomesparticularly troublesome in case a large number of instances of thealgorithm is needed, as may be the case when base stations serve manycells and when techniques like 4-way receiver diversity is introduced inthe WCDMA UL is introduced in the uplink. A recursive algorithm has beenintroduced to reduce the memory consumption. Relative to the slidingwindow algorithm, the recursive algorithm can reduce the memoryrequirement by a factor of more than one hundred.

Load Prediction without Other Cell Interference Estimation

Following is a discussion on techniques to predict instantaneous load onthe uplink air interface ahead in time. The scheduler uses thisfunctionality. The scheduler tests different combinations of grants todetermine the best combinations, e.g., maximizing the throughput. Thisscheduling decision will only affects the air interface load after anumber of TTIs (each such TTI a predetermined time duration such as 2 or10 ms), due to grant transmission latency and UE latency before the newgrant takes effect over the air interface.

In a conventional SIR (signal-to-interference ratio) based method, theprediction of uplink load, for a tentative scheduled set of UEs andgrants, is based on the power relation defined by:

$\begin{matrix}{{{{P_{RTWP}(t)} - {P_{N}(t)}} = {{\sum\limits_{i = 1}^{N}{{L_{i}(t)}{P_{RTWP}(t)}}} + {P_{othor}(t)}}},} & (5)\end{matrix}$

where L_(i)(t) is the load factor of the i-th UE of the own cell. Asindicated, P_(other)(t) denotes the other cell interference. The loadfactors of the own cell are computed as follows. First, note that:

$\begin{matrix}{{\left. \begin{matrix}{{\left( {C/I} \right)_{i}(t)} = \frac{P_{i}(t)}{{P_{RTWP}(t)} - {\left( {1 - \alpha} \right)P_{i}}}} \\{= \frac{{L_{i}(t)}{P_{RTWP}(t)}}{{P_{RTWP}(t)} - {\left( {1 - \alpha} \right){L_{i}(t)}{P_{RTWP}(t)}}}} \\{= \frac{L_{i}(t)}{1 - {\left( {1 - \alpha} \right){L_{i}(t)}}}}\end{matrix}\Leftrightarrow {L_{i}(t)} \right. = \frac{\left( {C/I} \right)_{i}(t)}{1 + {\left( {1 - \alpha} \right)\left( {C/I} \right)_{i}(t)}}},{i = 1},\ldots \mspace{14mu},I,} & (6)\end{matrix}$

where I is the number of UEs in the own cell and α is theself-interference factor. The carrier to interference values,(C/I)_(i)(t), i=1, . . . , I, are then related to the SINR (measured onthe DPCCH channel) as follows:

$\begin{matrix}{{{\left( {C/I} \right)_{i}(t)} = {\frac{{SINR}_{i}(t)}{W_{i}}\frac{RxLoss}{G} \times \; \left( {1 + \frac{{\beta_{{DPDCH},i}^{2}(t)} + {\beta_{{EDPCCH},i}^{2}(t)} + {{n_{{codes},i}(t)}{\beta_{{EDPDCH},i}^{2}(t)}} + {\beta_{{HSDPCCH},i}^{2}(t)}}{\beta_{DPCCH}^{2}(t)}} \right)}},\mspace{79mu} {i = 1},{\ldots \mspace{14mu} {I.}}} & (7)\end{matrix}$

In (7), W_(i) represents the spreading factor, RxLoss represents themissed receiver energy, G represents the diversity gain and the β:srepresent the beta factors of the respective channels. Here, inactivechannels are assumed to have zero data beta factors.

The UL load prediction then computes the uplink load of the own cell bya calculation of (6) and (7) for each UE of the own cell, followed by asummation:

$\begin{matrix}{{{L_{own}(t)} = {\sum\limits_{i = 1}^{I}\; {L_{i}(t)}}},} & (8)\end{matrix}$

which transforms (5) to:

P _(RTWP)(t)=L _(own)(t)P _(RTWP)(t)+P _(other)(t)+P _(N)(t).  (9)

Dividing (9) by P_(N)(t) shows that the RoT can be predicted k TTIsahead as:

$\begin{matrix}{{{RoT}\left( {t + {kT}} \right)} = {\frac{{P_{othor}(t)}/{P_{N}(t)}}{1 - {L_{own}(t)}} + {\frac{1}{1 - {L_{own}(t)}}.}}} & (10)\end{matrix}$

In the SIR based load factor calculation, the load factor L_(i)(t) isdefined by (6). However, in a power based load factor calculation, theload factor L_(i)(t) can be defined by:

$\begin{matrix}{{{L_{i}(t)} = \frac{P_{i}(t)}{P_{RTWP}(t)}},{i = 1},\ldots \mspace{14mu},I,} & (11)\end{matrix}$

and equations (8)-(10) may be calculated based on the load factorL_(i)(t) of (11) to predict the RoT k TTIs ahead. An advantage of thepower based load factor calculation is that the parameter dependency isreduced. But on the downside, a measurement of the UE power is needed.

In heterogeneous networks (HetNets), different kinds of cells are mixed.A problem that arises in Hetnets in that the cells are likely to havedifferent radio properties in terms of (among others):

radio sensitivity;

frequency band;

coverage;

output power;

capacity; and

acceptable load level.

This can be an effect of the use of different RBS sizes (macro, micro,pico, femto), different revisions (different receiver technology, SWquality), different vendors, the purpose of a specific deployment, andso on. An important factor in HetNets is that of the air interface loadmanagement, i.e., the issues associated with the scheduling of radioresources in different cells and the interaction between cells in termsof inter-cell interference.

These issues are exemplified with reference to FIG. 2 which illustratesa low power cell with limited coverage intended to serve a hotspot. Toenable sufficient coverage of the hot spot, an interference suppressingreceiver like the G-rake+ is used. One problem is now that the low powercell is located in the interior of and at the boundary of a specificmacro cell. Also, surrounding macro cells interfere with the low powercell rendering a high level of other cell interference in the low powercell which, despite the advanced receiver, reduces the coverage tolevels that do not allow coverage of the hot spot. As a result, UEs ofthe hot spot are connected to the surrounding macro cells, which canfurther increase the other cell interference experienced by the lowpower cell.

SUMMARY

A non-limiting aspect of the disclosed subject matter is directed to amethod performed in a radio network node of a wireless network fordetermining other cell interference applicable at a particular time. Themethod can comprise the step of estimating a load utilizationprobability p_(load)(t₁) based at least on a load utilizationprobability estimate {circumflex over (p)}_(load)(t₀) and aninterference-and-noise sum estimate {circumflex over(P)}_(other)(t₀)+{circumflex over (P)}_(N)(t₀) applicable at a time t₀to obtain a load utilization probability estimate {circumflex over(p)}_(load)(t₁) applicable at a time t₁, in which t₁−t₀=T>0. The methodcan also comprise the step of estimating an interference-and-noise sumP_(other)(t₁)+P_(N)(t₁) based at least on the load utilizationprobability estimate {circumflex over (p)}_(load)(t₀) and theinterference-and-noise sum estimate {circumflex over(P)}_(other)(t₀)+{circumflex over (P)}_(N)(t₀) to obtain aninterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) applicable at the timet₁. The method can further comprise the step of estimating an other cellinterference P_(other)(t₁) based at least on the interference-and-noisesum estimate {circumflex over (P)}_(other)(t₁)+{circumflex over(P)}_(N)(t₁) and a thermal noise estimate {circumflex over (P)}_(N)(t₁)to obtain an other cell interference estimate {circumflex over(P)}_(other)(t₁) applicable at the time t₁.

Another non-limiting aspect of the disclosed subject matter is directedto a non-transitory computer-readable medium which has stored thereinprogramming instructions. When a computer executes the programminginstructions, the computer executes the method performed in a radionetwork node of a wireless network as described above for determiningother cell interference applicable at a particular time.

Yet another non-limiting aspect of the disclosed subject matter isdirected to a radio network node of a wireless network. The radionetwork node is structured to determine other cell interferenceapplicable at a particular time. The radio network node can comprise atransceiver structured to transmit and receive wireless signals via oneor more antennas from and to one or more cell terminals located withinthe cell of interest, a communicator structured to communicate withother network nodes, and a scheduler structured to schedule uplinktransmissions from the cell terminals. The scheduler can also bestructured to estimate a load utilization probability p_(load)(t₁) basedat least on a load utilization probability estimate {circumflex over(p)}_(load)(t₀) and an interference-and-noise sum estimate {circumflexover (P)}_(other)(t₀)+{circumflex over (P)}_(N)(t₀) applicable at a timet₀ to obtain a load utilization probability estimate {circumflex over(p)}_(load)(t₁) applicable at a time t₁, wherein t₁−t₀=T>0. Thescheduler can further be structured to estimate aninterference-and-noise sum P_(other)(t₁)+P_(N)(t₁) based at least on theload utilization probability estimate {circumflex over (p)}_(load)(t₀)and the interference-and-noise sum estimate {circumflex over(P)}_(other)(t₀)+{circumflex over (P)}_(N)(t₀) to obtain aninterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) applicable at the timet₁. The scheduler can yet further be structured to estimate an othercell interference P_(other)(t₁) based at least on theinterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) and a thermal noiseestimate {circumflex over (P)}_(N)(t₁) to obtain an other cellinterference estimate {circumflex over (P)}_(other)(t₁) applicable atthe time t₁.

In these aspects, the load utilization probability p_(load)(t) canexpress a relationships between radio resource grants scheduled to oneor more cell terminals and radio resource grants used by the same cellterminals applicable at a time t. Each cell terminal can be a wirelessterminal in the cell of interest, and {circumflex over (p)}_(load)(t)can express an estimate of the load utilization probability p_(load)(t).The interference-and-noise sum P_(other)(t)+P_(N)(t) can express a sumof undesired signals, other than an own cell load P_(own)(t), applicableat the time t, and {circumflex over (P)}_(other)(t)+{circumflex over(P)}_(N)(t) can express an estimate of the interference-and-noise sumestimate P_(other)(t)+P_(N)(t). The own cell load P_(own)(t) can expressa sum of signals due to wireless activities in the cell of interest. Theother cell interference P_(other)(t) can express a sum of interferencespresent in the cell of interest due to wireless activities applicable atthe time t in one or more cells other than in the cell of interest, andP_(other)(t) can express an estimate of the other cell interferenceP_(other)(t). A thermal noise P_(N)(t) can express a sum of undesiredsignals present in the cell of interest at the time t other than the owncell load P_(own)(t) and other than the other cell interferenceP_(other)(t), and {circumflex over (P)}_(N)(t₁) can express an estimateof the thermal noise P_(N)(t).

DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features, and advantages of thedisclosed subject matter will be apparent from the following moreparticular description of preferred embodiments as illustrated in theaccompanying drawings in which reference characters refer to the sameparts throughout the various views. The drawings are not necessarily toscale.

FIG. 1 illustrates a conventional algorithm that estimates a noisefloor.

FIG. 2 illustrates an example scenario of a low power cell with limitedcoverage intended to serve a hotspot;

FIG. 3 illustrates a plot of a grant utilization probability;

FIG. 4 illustrates an example scenario in which other cell interferenceis determined;

FIGS. 5 and 6 respectively illustrate example embodiments of a radionetwork node;

FIG. 7 illustrates a flow chart of example method performed by a radionetwork node to determine an other cell interference;

FIG. 8 illustrates a flow chart of an example process performed by aradio network node to estimate the load utilization probability and toestimate the interference-and-noise sum;

FIG. 9 illustrates a flow chart of another example process performed bya radio network node to estimate the load utilization probability and toestimate the interference-and-noise sum;

FIG. 10 illustrates a flow chart of an example process performed by aradio network node to obtain an interference-and-noise sum estimate;

FIG. 11 illustrates a flow chart of an example process performed by aradio network node to determine a gain factor;

FIG. 12 illustrates a flow chart of an example process performed by aradio network node to determine an other cell interference estimate;

FIG. 13 illustrates a flow chart of yet another example processperformed by a radio network node to estimate the load utilizationprobability and to estimate the interference-and-noise sum; and

FIG. 14 illustrates a flow chart of an example process performed by aradio network node to perform a Kalman filter update of a predictedstate vector.

DETAILED DESCRIPTION

For purposes of explanation and not limitation, specific details are setforth such as particular architectures, interfaces, techniques, and soon. However, it will be apparent to those skilled in the art that thetechnology described herein may be practiced in other embodiments thatdepart from these specific details. That is, those skilled in the artwill be able to devise various arrangements which, although notexplicitly described or shown herein, embody the principles of thedescribed technology.

In some instances, detailed descriptions of well-known devices,circuits, and methods are omitted so as not to obscure the descriptionwith unnecessary details. All statements herein reciting principles,aspects, embodiments and examples are intended to encompass bothstructural and functional equivalents. Additionally, it is intended thatsuch equivalents include both currently known equivalents as well asequivalents developed in the future, i.e., any elements developed thatperform same function, regardless of structure.

Thus, for example, it will be appreciated that block diagrams herein canrepresent conceptual views of illustrative circuitry embodyingprinciples of the technology. Similarly, it will be appreciated that anyflow charts, state transition diagrams, pseudo code, and the likerepresent various processes which may be substantially represented incomputer readable medium and executed by a computer or processor,whether or not such computer or processor is explicitly shown.

Functions of various elements including functional blocks labeled ordescribed as “processors” or “controllers” may be provided throughdedicated hardware as well as hardware capable of executing associatedsoftware. When provided by a processor, functions may be provided by asingle dedicated processor, by a single shared processor, or by aplurality of individual processors, some of which may be shared ordistributed. Moreover, explicit use of term “processor” or “controller”should not be construed to refer exclusively to hardware capable ofexecuting software, and may include, without limitation, digital signalprocessor (shortened to “DSP”) hardware, read only memory (shortened to“ROM”) for storing software, random access memory (shortened to RAM),and non-volatile storage.

In this document, 3GPP terminologies—e.g., WCDMA, LTE—are used asexamples for explanation purposes. Note that the technology describedherein can be applied to non-3GPP standards, e.g., WiMAX, cdma2000,1xEVDO, etc. Thus, the scope of this disclosure is not limited to theset of 3GPP wireless network systems and can encompass many domains ofwireless network systems. Also, a base station (e.g., RBS, NodeB,eNodeB, eNB, etc.) will be used as an example of a radio network node inwhich the described method can be performed. However, it should be notedthat the disclosed subject matter is applicable to any node, such asrelay stations, that receive wireless signals. Also without loss ofgenerality, mobile terminals (e.g., UE, mobile computer, PDA, etc.) willbe used as examples of wireless terminals that communicate with the basestation.

As indicated above, one major disadvantage of many conventional RoT(t)estimation techniques is in the difficulty in separating the thermalnoise P_(N)(t) from the interference P_(other)(t) from other cells. Thismakes it difficult to estimate the RoT(t), i.e., difficult to estimatethe load as given in equation (1). The other cell interferenceP_(other)(t) in this context may be viewed as a sum of interferencespresent in a cell of interest due to wireless activities applicable attime t in one or more cells other than in the cell of interest. In oneor more aspects, the determination of the other cell interferenceP_(other)(t) involves estimating the other cell interference. For thepurposes of this disclosure, estimations of parameters are indicatedwith a “̂” (caret) character. For example, {circumflex over(P)}_(other)(t) may be read as an estimate of the other cellinterference P_(other) (t).

There are known techniques to determine the other cell interferenceestimate {circumflex over (P)}_(other)(t). These conventional techniquesassume that the powers of all radio links are measured in the uplinkreceiver. This assumption is not true in many instances today. The powermeasurement is associated with difficulties since:

-   -   In WCDMA for example, the uplink transmission is not necessarily        orthogonal, which can cause errors when the powers are        estimated; and    -   The individual code powers are often small, making the SNRs        (signal-to noise ratio) low as well. This further contributes to        the inaccuracy of the power estimates.

One major problem associated with the conventional other cellinterference estimation techniques is that the sum of other cellinterference and thermal noise P_(other)(t)+P_(N)(t) (referred to as theinterference-and-noise sum) needs to be estimated through high orderKalman filtering. The primary reason is that all powers of the UEs needto be separately filtered using at least one Kalman filter state per UEwhen such techniques are used. This step therefore is associated with avery high computational complexity. There are techniques that can reducethis computational complexity, but the complexity can be still too highwhen the number of UEs increases. In these conventional solutions, thethermal noise floor N(t) is estimated as described above, i.e.,{circumflex over (N)}(t) is determined followed by a subtraction toarrive at an estimate of the other cell interference {circumflex over(P)}_(other)(t).

In the existing solutions, the EUL utilizes a scheduler that aims tofill the load headroom of the air interface, so that the different UErequests for bitrates are met. As stated above, the air-interface loadin WCDMA is determined in terms of the noise rise over the thermal powerlevel, i.e., the RoT(t), which is estimated at the base station.

When evaluating scheduling decisions, the scheduler predicts the loadthat results form the scheduled grants, to make sure that the scheduledload does not exceed the load thresholds for coverage and stability.This can be complicated since the grant given to a UE only expresses alimit on the UL power the UE is allowed to use. However, the UE mayactually use only a portion of its grant. The conventional schedulermakes a worst case analysis, assuming that all UEs will use their grantsat all times. But in reality, UEs in general have a relatively lowutilization of grants. This is evident from field measurements as thosedepicted in FIG. 3. The plot indicates a grant utilization of only about25%. In other words, a significant amount (about 75%) of air-interfaceresources is wasted.

To summarize, the lack of technology for estimation of the loadutilization probability and its variance can have at least the followingdisadvantages:

-   -   Can lead to an underutilization of the air interface, due to the        fact that UEs often do not use all the power granted to them;    -   Can prevent the use of systematic statistical overbooking of        grants, since a statistical model of load utilization is not        available. In particular, a statistical model of variance in the        load utilization is not available; and    -   Can lead to a general inaccuracy of the load prediction, since        unmodelled receiver impairments are not captured correctly by a        load utilization probability estimate.

Regarding HetNets in particular, problems associated with conventionalscheduling techniques can be explained in a relatively straightforwardmanner. For scheduling in the base station in general, prior techniquesrequire measurement of all UE powers in the UL. This is very costlycomputationally, requiring Kalman filters of high order for processingthe measurements to obtain estimates of the other cell interferencepower. This is because each own cell UE adds a state to the Kalmanfilter. The consequence, if such estimation cannot be done, is that thescheduler is unaware of the origin of the interference, thereby makingit more difficult to arrive at good scheduling decisions. For HetNets,the problem is again that there is no information of the origin ofinterference, and interference variance, for adjacent cells. This isprimarily due to the lack of low complexity estimators for thesequantities.

Each of one or more aspects of the disclosed subject matter addressesone or more of the issues related to conventional techniques. Forexample, recall from above that in conventional scheduling techniques,there is a delay of some number of TTIs from the scheduling decisionuntil the interference power appears over the air interface. Thescheduler also bases its scheduling decisions on a prediction of theload of the traffic it schedules. Since the UEs do not always utilizepower granted by the scheduler, the load prediction are likely to beinaccurate. The inaccuracy tends to increase as the delay increases. Toaddress this issue, in one or more aspects of the disclosed subjectmatter, measurements of momentary load utilization may be made andaccounted for in the estimation of other cell interferences.

As another example, also recall that load prediction does not accountfor all imperfections in the modeling of the UL receiver. To addressthis issue, in one or more aspects of the disclosed subject matter, loadfactor bias may be estimated, e.g., when other cell interference isestimated.

A general concept applicable to one or more inventive aspects includes aUL nonlinear interference model and an estimator. The UL nonlinearinterference can be responsive to:

-   -   a. a scheduled own cell load factor L_(own)(t), an estimated        load utilization probability {circumflex over (p)}_(load)(t)        (note the lower case “p”), an estimated sum of other cell        interference and thermal noises {circumflex over        (P)}_(other)(t)+{circumflex over (P)}_(N)(t) (note the upper        case “P”), and (optionally) an estimated load factor bias        Δ{circumflex over (L)}_(own)(t), these quantities expressing an        UL load curve relationship; or    -   b. an estimated own cell load factor {circumflex over        (L)}_(own)(t), an estimated sum of other cell interference and        thermal noise {circumflex over (P)}_(other)(t)+{circumflex over        (P)}_(N)(t), and (optionally) an estimated load factor bias        Δ{circumflex over (L)}_(own)(t), these quantities expressing an        UL load curve relationship.

The estimator can be responsive to:

-   -   a. a measured total wideband power y_(RTWP)(t), a measured load        utilization probability p_(load)(t), a received uplink own cell        load factor L_(own)(t), and the UL nonlinear interference model;        or    -   b. a measured total wideband power y_(RTWP)(t), a measured own        cell load factor L_(own)(t), and the UL nonlinear interference        model.

The estimator can also be responsive to a dynamic model for propagationof the estimated states. The estimated states can include:

-   -   a. the estimated sum of other cell interference and thermal        noise {circumflex over (P)}_(other)(t)+{circumflex over        (P)}_(N)(t) the estimated load utilization probability        {circumflex over (p)}_(load)(t), (optionally) the estimated load        factor bias Δ{circumflex over (L)}_(own)(t) and at least one        delay line state; or    -   b. the estimated sum of other cell interference and thermal        noise {circumflex over (P)}_(other)(t)+{circumflex over        (P)}_(N)(t), the estimated own cell load factor L_(own)(t),        (optionally) the estimated load factor bias Δ{circumflex over        (L)}_(own)(t) and at least one delay line state.

The estimator can further be responsive to an estimated thermal noise{circumflex over (P)}_(N)(t), and provide an estimated other cellinterference {circumflex over (P)}_(other)(t). For example, the othercell interference estimate {circumflex over (P)}_(other)(t) may bearrived at by subtracting the thermal noise estimate {circumflex over(P)}_(N)(t) from the interference-and-noise sum estimate {circumflexover (P)}_(other)(t)+{circumflex over (P)}_(N)(t).

In the discussion above, the values of parameters are “estimated”,“measured”, “received” or “computed”. A measured value in essence can beviewed a number that expresses a value of a measured quantity. Anestimated value is not a number that expresses a value of a measurement,at least not directly. Rather, an estimate can be viewed as a processedset of measurements, e.g., by some filtering operation. There can alsobe received and/or computed quantities, such as time varying parametersthat are obtained from other sources. It is stressed that measured orestimated quantities can be very different, also in case the measuredand estimated quantity refer to the same underlying physical quantity,e.g., a specific power. One among many reasons for this is that theprocessing to obtain estimates e.g., may combine measurements fromdifferent times to achieve e.g., noise suppression and bias reduction.

Also in the discussion above, “a” and “b” represent alternativeembodiments. In the discussion below, alternative “a” will be describedin detail, and some comments on the difference between the alternativewill be provided.

As will be demonstrated below, one very significant advantage of theinventive estimator is its low order and associated low computationalcomplexity. In one embodiment, the estimator can be a variant of anextended Kalman filter (EKF), arranged for processing using thenonlinear interference model.

One or more of the inventive aspects can be applied to both the slidingwindow and recursive RoT estimation algorithms. Either SIR or powerbased load factor calculation may be used. The power based calculationis preferred however.

Recall from the discussion regarding HetNets that the surrounding macrocells can interfere with the low power cell to levels such that the UEsof the hotspot are actually connected to the macro cells. To addresssuch issues, in one or more aspects of disclosed subject matter, RNC(radio network controller) or the surrounding RBSs can be informed ofthe interference situation and can take action as appropriate. Forexample, admission control in the RNC or functionalities in thesurrounding RBSs can be used to reduce the other cell interference andprovide better management of the hot spot traffic, e.g., in terms of airinterface load. To enable this to take place, the RBS can includecapabilities to estimate the other cell interference.

FIG. 4 illustrates an example scenario in which a radio network node 410(e.g., eNB, eNode B, Node B, base station (BS), radio base station(RBS), and so on) can estimate the other cell interference. In thefigure, the radio network node 410 serves one or more wireless terminals430 (e.g., user equipment, mobile terminal, laptops, M2M(machine-to-machine) terminals, etc.) located within a correspondingcell 420. For clarity, the radio network node 410 will be referred to asan own radio network node, the cell 420 will be referred to as the cellof interest, and the terminals 430 within the cell of interest 420 willbe referred to as own terminals. Uplink signaling and data traffic fromthe own terminals 430 to the own radio network node 410 are illustratedas solid white arrows.

The scenario in FIG. 4 also includes other radio network nodes 415serving other wireless terminals 435 as indicated by dashed whitearrows. When the other terminals 435 transmit to their respective otherradio network nodes 415, these signals are also received in the ownradio network node 410 as indicated by shaded solid arrows. Such signalsact as interferers within the cell of interest 420. A sum of powers ofthese interfering signals experienced at the own radio network node 410at time t will be denoted as P_(other)(t). In other words, the othercell interference P_(other)(t) may be viewed as expressing a sum ofinterferences present in the cell of interest due to wireless activitiesapplicable at time t in one or more cells other than in the cell ofinterest 420. Further, there is a large solid white arrow with noparticular source. This represents the thermal noise P_(N) (t)experienced in the own radio network node 410 of the cell of interest420 at time t.

FIG. 5 illustrates an example embodiment of a radio network node 410.The radio network node 410 may comprise several devices including acontroller 510, a transceiver 520, a communicator 530 and a scheduler540. The transceiver 520 may be structured to wirelessly communicatewith wireless terminals 430. The communicator 530 may be structured tocommunicate with other network nodes and with core network nodes. Thecontroller 510 may be structured to control the overall operations ofthe radio network node 410.

FIG. 5 provides a logical view of the radio network node. It is notstrictly necessary that each device be implemented as physicallyseparate modules or circuits. Some or all devices may be combined in aphysical module. Also, one or more devices may be implemented inmultiple physical modules as illustrated in FIG. 6.

The devices of the radio network node 410 as illustrated in FIG. 5 neednot be implemented strictly in hardware. It is envisioned that any ofthe devices maybe implemented through a combination of hardware andsoftware. For example, as illustrated in FIG. 6, the radio network node410 may include one or more central processing units 610 executingprogram instructions stored in a storage 620 such as non-transitorystorage medium or firmware (e.g., ROM, RAM, Flash) to perform thefunctions of the devices. The radio network node 410 may also include atransceiver 520 structured to receive wireless signals from the wirelessterminals 430 and to send signals to the wireless terminals 430 over oneor more antennas 525 in one or more channels. The radio network node 410may further include a network interface 630 to communicate with othernetwork nodes such as the core network nodes.

In one or more aspects, the radio network node 410 can be structured toimplement a high performing estimator. The inventive estimator canperform a joint estimation of P_(othor)(t)+P_(N)(t), P_(N)(t),P_(other)(t) (note the upper case “P”) and the load utilizationprobability p_(load) (t) (note the lower case “p”). An extended Kalmanfilter (EKF) can be used in one or more embodiments of the proposedestimator.

The proposed estimator can use any one or more of the followinginformation:

-   -   Measurements of P_(RTWP)(t), with a sampling rate of        T_(RTWP)=k_(RTWP)TTI, k_(RTWP)εZ+. Preferably, the measurements        are available for each antenna branch.    -   Computed load factors L_(own)(t), with a sampling rate of        T_(L)=k_(L)TTI, k_(L)εZ+. Preferably, load factors are available        per cell and are valid on cell level. They need not necessarily        be valid on antenna branch level with Rx diversity.    -   The loop delay T_(D) between the calculation of L_(own)(t), and        the time it takes effect on the air interface. The loop delay        may be dependent on the TTI. Preferably, the loop delay is        available for and valid per cell.    -   Measured load factors L _(own)(t), with a sampling rate of T        _(L) =k _(L) TTI, k _(L) εZ+. Preferably, the load factors are        available per cell, and valid on the cell level. They need not        necessarily be valid on the antenna branch level with Rx        diversity. The factors can be obtained after TFCI decoding.    -   The loop delay T _(D) between the calculation of L _(own)(t),        and the time it takes effect on the air interface. The loop        delay can be dependent on the TTI and larger than T_(D) since        the measured load factor calculation may necessitate TFCI and        E-TFCI decoding.

For adaptation to extended Kalman filtering, the following states aremodeled:

x ₁(t)=p _(load)(t)—load utilization probability at time t,  (12)

x ₂(t)=P _(other)(t)+P _(N)(t)—interference-and-noise sum at timet,  (13)

x ₃(t)=Δ T _(own)(t)—load factor bias at time t,  (14)

x ₄(t)=x ₁(t−T)—decoding delay incorporated.  (15)

Modeling in one aspect may be viewed as a form of state space modelingin which state space of a physical system is mathematically modeled as aset of input, output and state variables related by equations.

Since an additional decoding delay affects the loop, the first statex₁(t) should be delayed by an extra state to define the fact that theload utilization probability measurement is subject to an additionaldelay T for decoding. The fourth state x₄(t) can be used for thispurpose. The delay T can any positive integer multiple of the TTI.Typically the delay T is substantially equal to one TTI. In theequations for the states, ΔL_(own)(t) represents a slowly varying loadfactor bias error in the measurement model.

If alternative “b” is used, then the estimated own cell load factorL_(own)(t) may be introduced as the first state x₁(t).

In the inventive nonlinear model, various measurements can be madeavailable for processing. First of these is the total wideband powerP_(RTWP)(t). Note that the scheduled load of the own cell L_(own)(t) isa computed quantity (e.g., based on SINR measurements). For this reason,it is advantageous to provide a measurement model of P_(RTWP)(t),expressed in terms of the states, computed quantities and a measurementuncertainty. Towards this end, first note that the load in equation (6)does not account for the load utilization probability p_(load)(t). Also,it does not account for the delay T_(D).

To model the load utilization effect, and to compensate forsemi-constant load factor errors, a review of equation (5) suggests thatload underutilization can be modeled by a modification of (5) and (6)as:

$\begin{matrix}\begin{matrix}{{L_{{own},{utilized}}(t)} = {{\sum\limits_{i = 1}^{I}\; {{p_{load}(t)}{L_{i}\left( {t - T_{D}} \right)}}} + {\Delta \; {{\overset{\_}{L}}_{own}(t)}}}} \\{{= {{{p_{load}(t)}{L_{own}\left( {t - T_{D}} \right)}} + {\Delta \; {{\overset{\_}{L}}_{own}(t)}}}},}\end{matrix} & (16) \\{{P_{RTWP}(t)} = {{{L_{{own},{utilized}}(t)}{P_{RTWP}(t)}} + {P_{other}(t)} + {P_{N}(t)}}} & (17)\end{matrix}$

which results in

$\begin{matrix}{{P_{RTWP}(t)} = {\frac{1}{1 - {{L_{own}\left( {t - T_{D}} \right)}{p_{load}(t)}} + {\Delta \; {{\overset{\_}{L}}_{own}(t)}}}{\left( {{P_{other}(t)} + {P_{N}(t)}} \right).}}} & (18)\end{matrix}$

After an addition of a zero mean white measurement noise e_(RTWP)(t) andreplacement of variables by the states of (12)-(15), the followingnonlinear measurement equations result:

$\begin{matrix}{{{y_{RTWP}(t)} = {\frac{x_{2}(t)}{1 - {{L_{own}\left( {t - T_{D}} \right)}{x_{1}(t)}} + {x_{3}(t)}}{e_{RTWP}(t)}}},} & (19) \\{{R_{2,{RTWP}}(t)} = {{E\left\lbrack {e_{RTWP}^{2}(t)} \right\rbrack}.}} & (20)\end{matrix}$

In (19) and (20), y_(RTWP)(t)=P_(RTWP)(t) and R_(2,RTWP)(t) denotes the(scalar) covariance matrix of e_(RTWP)(t). If the load of the own cellis computed using both EUL and R99 traffic, the delay can be valid forboth. If the own cell load is estimated instead, L_(own)(t−T_(D))x₁(t)can be expressed by a state directly modeling the estimated load factorof the own cell. The own cell load factor appearing in (19) can betreated as a known time varying factor, not as an estimate.

Note that (19) can represents a nonlinear load curve, expressed in termsof the estimated load utilization probability (x₁(t)), the estimated sumof neighbor cell interference and thermal noise power (x₂(t)) and theestimated load factor bias (x₃(t)). That is, (19) can represent anonlinear curve expressed in terms of {circumflex over (x)}₁(t),{circumflex over (x)}₂(t) and {circumflex over (x)}₃(t). Further thecomputed (“received”) load factor can be used in the nonlinear loadcurve. Equation (19) can be said to relate the momentary combined effectof the estimated quantities and received quantities to the left handside of the equation, i.e. the momentary measurement of the widebandpower. Note that in one or more embodiments, the thermal noise floorN(t) can be used to represent the thermal noise P_(N)(t) and the thermalnoise floor estimate {circumflex over (N)}(t) can be used to representthermal noise estimate {circumflex over (P)}_(N)(t) in these equations.

Measurement of the load utilization probability p_(load) (t) can be madeavailable per cell. As an example, the decoded TFCIs and E-TFCISs showwhich grants the wireless terminal 430 actually used in the last TTI.This provides the information needed to compute the actual load factorof the last TTI, i.e. to compute:

$\begin{matrix}{{p_{load}(t)} = {\frac{{\overset{\_}{L}}_{own}\left( {t - T_{D}} \right)}{L_{own}\left( {t - T_{D}} \right)}.}} & (21)\end{matrix}$

With such modification, the measurement model for the load utilizationprobability measurement becomes:

y _(loadUtilization)(t)=x ₄(t)+e _(loadUtilization)(t)  (22)

R _(2,loadUtilization)(t)=E[e _(loadUtilization)(t)]².  (23)

The transformation (21) can be view as essentially replacing the grantedload factor, L_(own)(t−T_(D)), with the load factor computed based onthe received TFCIs and E-TFCIs,

In the dynamic state model, random walk models can be adapted for thefirst and second state variables x₁(t) and x₂(t). In order to avoid adrifting bias correction of the load factor, an autoregressive model canbe used for the third state x₃(t). A further motivation for this is thatthe state can be expected to model errors that over an ensemble has azero mean. Hence the following state model can result from the states of(12)-(15).

$\begin{matrix}{{{x\left( {t + T_{TTI}} \right)} \equiv \begin{pmatrix}{x_{1}\left( {t + T} \right)} \\{x_{2}\left( {t + T} \right)} \\{x_{3}\left( {t + T} \right)} \\{x_{4}\left( {t + T} \right)}\end{pmatrix}} = {{\begin{pmatrix}1 & 0 & 0 & 0 \\0 & 1 & 0 & 0 \\0 & 0 & a & 0 \\1 & 0 & 0 & 0\end{pmatrix}\begin{pmatrix}{x_{1}(t)} \\{x_{2}(t)} \\{x_{3}(t)} \\{x_{4}(t)}\end{pmatrix}} + \begin{pmatrix}{w_{1}(t)} \\{w_{2}(t)} \\{w_{3}(t)} \\{w_{4}(t)}\end{pmatrix}}} & (24) \\{{R_{1}(t)} = {{E\left\lbrack {\begin{pmatrix}{w_{1}(t)} \\{w_{2}(t)} \\{w_{3}(t)} \\{w_{4}(t)}\end{pmatrix}\left( {{w_{1}(t)}\mspace{14mu} {w_{2}(t)}\mspace{14mu} {w_{4}(t)}\mspace{14mu} {w_{4}(t)}} \right)} \right\rbrack}.}} & (25)\end{matrix}$

Preferably, the delay T equals one TTI, but can be any positive integermultiple of the TTI. Note that by setting a=1, a random walk model canbe obtained for all states. A diagonal covariance matrix can be used.The last component of the system noise is preferably selected to be verysmall, reflecting the pure delay it is intended to model.

A general state space model behind the EKF can be expressed as follows:

x(t+T)=A(t)x(t)+B(t)u(t)+w(t).  (26)

y(t)=c(x(t))+e(t).  (27)

Here x(t) denotes a state vector, u(t) denotes an input vector (not usedin the inventive filtering), y(t) denotes an output measurement vectorcomprising power measurements performed in a cell (i.e., the totalreceived wideband power P_(RTWP)(t)), w(t) denotes the so called systemsnoise that represent the model error, and e(t) denotes the measurementerror. The matrix A(t) is a system matrix describing the dynamic modes,the matrix B(t) is the input gain matrix, and the vector c(x(t)) is the,possibly nonlinear, measurement vector which is a function of the statesof the system. Finally, t represents the time and T represents thesampling period.

The general case with a nonlinear measurement vector is considered here.For this reason, the extended Kalman filter should be applied. Thisfilter is given by the following matrix and vector iterationsInitialization:

t = t₀ x̂(0−1) = x₀ P(0−1) = P₀ Iteration t = t + T${C(t)} = {{\frac{\partial{c(x)}}{\partial x}_{x = {\hat{x}{({t{t - T}})}}}{\hat{x}\left( {tt} \right)}} = {{\hat{x}\left( {t{t - T}} \right)} + {{K_{f}(t)}\left( {{y(t)} - {c\left( {\hat{x}\left( {t{t - T}} \right)} \right)}} \right)}}}$P(tt) = P(tt − T) − K_(f)(t)C(t)P(tt − T)x̂(t + Tt) = A x̂(tt) + B u(t)

$\begin{matrix}{{P\left( {{t + T}t} \right)} = {{{{AP}\left( {tt} \right)}A^{T}} + {R_{1}.{End}.}}} & (28)\end{matrix}$

The quantities introduced in the filter iterations (28) are differenttypes of estimates ({circumflex over (x)}(t|t−T), {circumflex over(x)}(t|t), P(t|t−T), and P(t|t)), function of such estimates (C(t) andK_(f)(t)), or other quantities (R₂(t) and R₁(t)), defined as follows:

-   -   {circumflex over (x)}(t|t−T) denotes a state prediction, based        on data up to time t−T,    -   {circumflex over (x)}(t|t) denotes a filter update, based on        data up to time t,    -   P(t|t−T) denotes a covariance matrix of the state prediction,        based on data up to time t−T,    -   P(t|t) denotes a covariance matrix of the filter update, based        on data up to time t,    -   C(t) denotes a linearized measurement matrix (linearization        around the most current state prediction),    -   K_(f)(t) denotes a time variable Kalman gain matrix,    -   R₂(t) denotes a measurement covariance matrix, and    -   R₁(t) denotes a system noise covariance matrix.

Note that R₁(t) and R₂(t) are often used as tuning variables of thefilter. In principle, the bandwidth of the filter can be controlled bythe matrix quotient of R₁(t) and R₂(t).

An example of an inventive estimation scheme using EKF will bedescribed. The quantities of the EKF for estimation of the other cellinterference and the load utilization load factor bias can now bedefined. Using (19)-(20) and (22)-(25) and (28) it follows that:

$\begin{matrix}{\mspace{79mu} {{C(t)} = \begin{pmatrix}{C_{11}(t)} & {C_{12}(t)} & {C_{13}(t)} & 0 \\0 & 0 & 0 & {C_{24}(t)}\end{pmatrix}}} & (29) \\{\mspace{79mu} {{C_{11}(t)} = \frac{{L_{own}\left( {t - T_{D}} \right)}{{\hat{x}}_{2}\left( {t{t - T}} \right)}}{\left( {1 - {{L_{own}\left( {t - T_{D}} \right)}{{\hat{x}}_{1}\left( {t{t - T}} \right)}} + {{\hat{x}}_{3}\left( {t{t - T}} \right)}} \right)^{2}}}} & (30) \\{\mspace{79mu} {{C_{12}(t)} = \frac{1}{1 - {{L_{own}\left( {t - T_{D}} \right)}{{\hat{x}}_{1}\left( {t{t - T}} \right)}} + {{\hat{x}}_{3}\left( {t{t - T}} \right)}}}} & (31) \\{\mspace{79mu} {{C_{13}(t)} = {- \frac{{\hat{x}}_{2}\left( {t{t - T}} \right)}{\left( {1 - {{L_{own}\left( {t - T_{D}} \right)}{{\hat{x}}_{1}\left( {t{t - T}} \right)}} + {{\hat{x}}_{3}\left( {t{t - T}} \right)}} \right)^{2}}}}} & (32) \\{\mspace{79mu} {{C_{24}(t)} = 1}} & (33) \\{{R_{2}(t)} = {{E\left\lbrack {\begin{pmatrix}{e_{RTWP}(t)} \\{e_{{load}\; {Utilization}}(t)}\end{pmatrix}\left( {{e_{RTWP}(t)}\mspace{14mu} {e_{{load}\; {Utilization}}(t)}} \right)} \right\rbrack}\begin{pmatrix}{R_{2,11}(t)} & {R_{2,12}(t)} \\{R_{2,12}(t)} & {R_{2,22}(t)}\end{pmatrix}}} & (34) \\{{{c\left( {\hat{x}\left( {t{t - T_{TTI}}} \right)} \right)} = \begin{pmatrix}\frac{{\hat{x}}_{2}\left( {t{t - T}} \right)}{1 - {{L_{own}\left( {t - T_{D}} \right)}{{\hat{x}}_{1}\left( {t{t - T}} \right)}} + {{\hat{x}}_{3}\left( {t{t - T}} \right)}} \\{{\hat{x}}_{4}\left( {{tt} - T} \right)}\end{pmatrix}}} & (35) \\{\mspace{79mu} {A = \begin{pmatrix}1 & 0 & 0 & 0 \\0 & 1 & 0 & 0 \\0 & 0 & a & 0 \\1 & 0 & 0 & 0\end{pmatrix}}} & (36) \\{\mspace{79mu} {B = 0}} & (37) \\\begin{matrix}{\mspace{79mu} {{R_{1}(t)} = {E\left\lbrack {\begin{pmatrix}{w_{1}(t)} \\{w_{2}(t)} \\{w_{3}(t)} \\{w_{4}(t)}\end{pmatrix}\left( {{w_{1}(t)}\mspace{14mu} {w_{2}(t)}\mspace{14mu} {w_{3}(t)}\mspace{14mu} {w_{4}(t)}} \right)} \right\rbrack}}} \\{= {\begin{bmatrix}{R_{1,11}(t)} & {R_{1,12}(t)} & {R_{1,13}(t)} & {R_{1,14}(t)} \\{R_{1,12}(t)} & {R_{1,22}(t)} & {R_{1,23}(t)} & {R_{1,24}(t)} \\{R_{1,13}(t)} & {R_{1,23}(t)} & {R_{1,33}(t)} & {R_{1,34}(t)} \\{R_{1,14}(t)} & {R_{1,24}(t)} & {R_{1,34}(t)} & {R_{1,44}(t)}\end{bmatrix}.}}\end{matrix} & (38)\end{matrix}$

In order to execute the EKF, the state prediction and the statecovariance prediction at time t are needed, they are given by thefollowing equations:

$\begin{matrix}{\mspace{79mu} {{\hat{x}\left( {t{t - T_{TTI}}} \right)} = \begin{pmatrix}{{\hat{x}}_{1}\left( {t{t - T}} \right)} \\{{\hat{x}}_{2}\left( {t{t - T}} \right)} \\{{\hat{x}}_{3}\left( {t{t - T}} \right)} \\{{\hat{x}}_{4}\left( {t{t - T}} \right)}\end{pmatrix}}} & (39) \\{{P\left( {t{t - T_{TTI}}} \right)} = {\begin{pmatrix}{P_{11}\left( {t{t - T}} \right)} & {P_{12}\left( {t{t - T}} \right)} & {P_{13}\left( {t{t - T}} \right)} & {P_{14}\left( {t{t - T}} \right)} \\{P_{12}\left( {t{t - T}} \right)} & {P_{22}\left( {t{t - T}} \right)} & {P_{23}\left( {t{t - T}} \right)} & {P_{24}\left( {t{t - T}} \right)} \\{P_{13}\left( {t{t - T}} \right)} & {P_{23}\left( {t{t - T}} \right)} & {P_{33}\left( {t{t - T}} \right)} & {P_{34}\left( {t{t - T}} \right)} \\{P_{14}\left( {t{t - T}} \right)} & {P_{24}\left( {t{t - T}} \right)} & {P_{34}\left( {t{t - T}} \right)} & {P_{44}\left( {t{t - T}} \right)}\end{pmatrix}.}} & (40)\end{matrix}$

The equations (29)-(40) define the EKF completely, when inserted in(28). The final step to compute the other cell interference estimate canbe:

P _(other)(t|t)={circumflex over (x)} ₂(t|t)−{circumflex over (P)}_(N)(t|t).  (41)

FIG. 7 illustrates a flow chart of example method 700 performed by aradio network node 410 to implement a high performing estimator. Themethod 700 may be performed by the scheduler 540, e.g., as loadestimation functionality associated with the scheduler, to determine theother cell interference P_(other)(t). In particular, the other cellinterference estimate {circumflex over (P)}_(other)(t) can bedetermined. The other cell interference P_(other)(t) can express a sumof interferences present in the cell of interest 420 due to wirelessactivities applicable at the time t in one or more cells other than inthe cell of interest.

As illustrated, in step 710, the radio network node 410, and inparticular the scheduler 540, can estimate the load utilizationprobability p_(load)(t₁) to obtain a load utilization probabilityestimate {circumflex over (p)}_(load)(t₁) applicable at a time t=t₁. Theestimation can be made based on at least on a load utilizationprobability estimate {circumflex over (p)}_(load)(t₀) and aninterference-and-noise sum estimate {circumflex over(P)}_(other)(t₀)+{circumflex over (P)}_(N)(t₀) applicable at time t=t₀.It should be noted that the term “t” enclosed in parentheses in theexpressions without subscripts (e.g., P_(other)(t), p_(load)(t), etc.)is intended to indicate time variable in general, and the same term “t”enclosed in parentheses with subscripts (e.g., P_(other)(t₀),p_(load)(t₁), etc.) is intended to indicate a particular time. Thus,time t₁ may also be viewed as t=t₁ for example.

The particular times t₀ and t₁ are assumed such that t₁−t₀=T>0. T canrepresent a duration between estimation times. In an embodiment, T is apositive integer multiple of a transmission time interval, preferablyone (e.g., for 10 ms TTI) but can be larger (e.g., 5 for 2 ms TTI). Inthe method 700, it can be assumed the values of the quantities at timet=t₀ (or simply at time t₀) are known (have been measured, computed,received, or otherwise have been determined), and the values of one ormore quantities at time t=t₁ are estimated or otherwise predicted.

In step 720, the radio network node 410 can estimate theinterference-and-noise sum P_(other)(t₁)+P_(N)(t₁) to obtain theinterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) applicable at the timet=t₁. This estimation can be made based at least on the load utilizationprobability estimate {circumflex over (p)}_(load)(t₀) and theinterference-and-noise sum estimate {circumflex over(P)}_(other)(t₀)+{circumflex over (P)}_(N)(t₀).

FIG. 8 illustrates a flow chart of an example process performed by theradio network node 410 to implement the steps 710 and 720 to obtain theload utilization probability estimate {circumflex over (p)}_(load)(t₁)and to obtain the interference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁). In step 810, a scheduledload factor L_(own)(t₁−T_(D)) can be calculated. Here, T_(D) canrepresent a delay between the calculation of the scheduled load factorand a time the schedule takes effect on an air interface. The scheduledload factor L_(own)(t−T_(D)) can express an amount of the radio resourcegrants scheduled to be used by the cell terminals 430 for uplinktransmissions at the time t.

In step 820, a used load factor L _(own)(t₁−T_(D)) can be obtained. Notethat the used load factor L _(own)(t−T_(D)) can express an amount of thescheduled radio resource grants used by the cell terminals 430 for theuplink transmissions at the time t.

In step 830, a load utilization

$\frac{{\overset{\_}{L}}_{own}\left( {t_{1} - T_{D}} \right)}{L_{own}\left( {t_{1} - T_{D}} \right)}$

can be measured or otherwise determined. Based on the measured loadutilization

$\frac{{\overset{\_}{L}}_{own}\left( {t_{1} - T_{D}} \right)}{L_{own}\left( {t_{1} - T_{D}} \right)},$

the load utilization probability estimate {circumflex over(p)}_(load)(t₁, can be obtained in step 840 and theinterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) can be obtained in step850.

FIG. 9 illustrates a flow chart of another example process performed bythe radio network node 410 to implement the steps 710 and 720 to obtainthe load utilization probability estimate {circumflex over(p)}_(load)(t₁) and to obtain the interference-and-noise sum estimate{circumflex over (P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁). In step910, a total wideband power y_(RTWP)(t₁) can be measured. Based on themeasured total wideband power y_(RTWP)(t₁), the load utilizationprobability estimate {circumflex over (p)}_(load)(t₁) can be obtained instep 920, and the interference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) can be obtained in step930.

FIG. 10 illustrates a flow chart of an example process performed by theradio network node 410 to implement the step 930 to obtain theinterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁). In step 1010, a gainfactor g(t₁) can be determined based on the load utilization probabilityestimate {circumflex over (p)}_(load)(t₁) and the scheduled load factorL_(own)(t₀). In step 1020, the measured total wideband powery_(RTWP)(t₁) can be modeled as a combination of theinterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) weighted by the gainfactor g(t₁) and a measurement uncertainty e_(RTWP)(t₁). Based on themeasured total wideband power y_(RTWP)(t₁) and the modeling thereof, theinterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) can be obtained.

FIG. 11 illustrates a flow chart of an example process performed by theradio network node 410 to implement the step 1010 to determine the gainfactor g(t₁). In step 1110, a load factor bias ΔL_(own)(t₁) can bedetermined. The load factor bias ΔL_(own)(t) can express an error of thescheduled load factor L_(own)(t). In step 1120, the gain factor g(t₁)can be determined based on the based at least on the load utilizationprobability estimate {circumflex over (p)}_(load)(t₁), the scheduledload factor L_(own)(t₀), and the load factor bias ΔL_(own)(t₁).

Referring back to FIG. 7, once the interference-and-noise sum estimate{circumflex over (P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) isdetermined in step 720, the radio network node 410 can estimate theother cell interference P_(other)(t₁) to obtain the other cellinterference estimate {circumflex over (P)}_(other)(t₁). The estimationcan be based at least on the interference-and-noise sum estimate{circumflex over (P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) and athermal noise estimate {circumflex over (P)}_(N)(t₁). Note that theinterference-and-noise sum P_(other)(t)+P_(N)(t) can express a sum ofundesired signals, other than an own cell load P_(own)(t). In FIG. 4,the interference-and-noise sum P_(other)(t)+P_(N)(t) are visuallyillustrated with shaded arrows (from the other terminals 435) and thelarge white arrow.

It can then be seen that once the once the interference-and-noise sumestimate {circumflex over (P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁)is determined, the other cell interference estimate {circumflex over(P)}_(other)(t) can be arrived at if the thermal noise {circumflex over(P)}_(N)(t) can be determined. FIG. 12 illustrates a flow chart of anexample process performed by the radio network node 410 to implement thestep 730 of estimating the other cell interference P_(other)(t₁). Instep 1210, the thermal noise estimate {circumflex over (P)}_(N)(t₁) canbe obtained. In one embodiment, a thermal noise floor estimate{circumflex over (N)}(t₁) corresponding to the cell of interest 420 canbe obtained as the thermal noise estimate {circumflex over (P)}_(N)(t₁).In step 1220, thermal noise estimate {circumflex over (P)}_(N)(t₁) canbe subtracted from the interference-and-noise sum estimate {circumflexover (P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) to obtain the othercell interference estimate {circumflex over (P)}_(other)(t₁).

FIG. 13 illustrates another flow chart of an example process performedby the radio network node 410 to implement the steps 710 and 720 toobtain the load utilization probability estimate {circumflex over(p)}_(load)(t₁) and to obtain the interference-and-noise sum estimate{circumflex over (P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁). FIG. 13may be viewed as a specific instance of the flow chart illustrated inFIG. 8. In FIG. 13, the extended Kalman filtering adapted for estimationis used.

In step 1310, the load utilization probability p_(load)(t) and theinterference-and-noise sum P_(other)(t)+P_(N)(t) can be modeled as firstand second states x₁(t)=p_(load)(t), x₂(t)=P_(other)(t)+P_(N)(t) in astate vector x(t) of a state space model.

In this context, the state space model can be characterized throughequations x(t+T)=A(t)x(t)+B(t)u(t)+w(t) and y(t)=c(x(t))+e(t). In theseequations, x(t) represents the state vector, u(t) represents an inputvector, y(t) represents the output measurement vector, w(t) represents amodel error vector, e(t) represents a measurement error vector, A(t)represents a system matrix describing dynamic modes of the system, B(t)represents an input gain matrix, c(x(t)) represents a measurement vectorwhich is a function of the states of the system, t represents the timeand T represents a sampling period. Thus, it is seen that modelingerrors and measurement errors are incorporated in the state space model.

In step 1320, the measured total wideband power y_(RTWP)(t) and themeasured load utilization y_(loadUtilization)(t) can be modeled in theoutput measurement vector y(t) of the state space model.

In step 1330, a predicted state vector {circumflex over (x)}(t₁|t₀) canbe obtained. The predicted state vector {circumflex over (x)}(t₁|t₀)includes first and second predicted states {circumflex over(x)}₁(t₁|t₀), {circumflex over (x)}₂(t₁|t₀) whose values are based onthe load utilization probability estimate {circumflex over(p)}_(load)(t₀) and the interference-and-noise sum estimate {circumflexover (P)}_(other)(t₀)+{circumflex over (P)}_(N)(t₀). In this context,the predicted state vector {circumflex over (x)}(t|t−T) denotes aprediction of the state vector x(t) based on information available up totime t−T. Recall from above that t₁−t₀=T>0. Thus, the predicted statevector {circumflex over (x)}(t₁|t₀) denotes a prediction the statevector x(t) at time t=t₁ based on information available up to time t=t₀.The time t=t₀ can be a time of initialization or a time of a previousiteration.

In step 1340, the predicted state vector {circumflex over (x)}(t₁|t₀)can be updated based on one or more measurements included in an outputmeasurement vector y(t₁) applicable at the time t=t₁, to obtain anestimated state vector {circumflex over (x)}(t₁|t₁)={circumflex over(x)}(t₁). The measurements can include the measured received totalwideband power y_(RTWP)(t₁) and the load utilization y_(load)(t₁). Thesolid white arrow entering the step 1340 in FIG. 13 is to indicate thatmeasurements may come into the step. Generally, the estimated statevector {circumflex over (x)}(t|t)={circumflex over (x)}(t) denotes anestimate of the state vector x(t) based on information available up totime t. This step corresponds to an adjusting step of the Kalman filteralgorithm in which the prediction made in the previous time (e.g., attime t=t₀) is adjusted according to measurements made in the currenttime (e.g., at time t=t₁).

In step 1350, first and second estimated states {circumflex over(x)}₁(t₁), {circumflex over (x)}₂(t₁) can be obtained from the estimatedstate vector {circumflex over (x)}(t₁) respectively as the loadutilization probability estimate {circumflex over (x)}₁(t₁)={circumflexover (p)}_(load)(t₁) and the interference-and-noise sum estimate{circumflex over (x)}₂(t₁)={circumflex over (P)}_(other)(t₁)+{circumflexover (P)}_(N)(t₁).

In step 1360, the estimated state vector {circumflex over (x)}(t₁) isprojected based at least on dynamic modes corresponding to the cell ofinterest to obtain a predicted state vector {circumflex over(x)}(t₂|t₁), t₂−t₁=T. Here, the predicted state vector {circumflex over(x)}(t₂|t₁) includes first and second predicted states {circumflex over(x)}₁(t₂|t₁) and {circumflex over (x)}₂(t₂|t₁) whose values are based onthe load utilization probability estimate {circumflex over(p)}_(load)(t₁) and the interference-and-noise sum estimate {circumflexover (x)}₂(t₁)={circumflex over (P)}_(other)(t₁)+{circumflex over(P)}_(N)(t₁). This step corresponds to a predicting step of the Kalmanfilter algorithm in which future states are predicted based on currentinformation. As seen, the steps in FIG. 13 can be iteratively performed.

In one embodiment, the steps 1340 and 1360 of updating the predictedstate vector {circumflex over (x)}(t₁|t₀) and of projecting theestimated state vector {circumflex over (x)}(t₁|t₁) comprise performinga Kalman filter process to iteratively predict and update the statevector x(t) to obtain the estimated state vector {circumflex over(x)}(t). Here, the estimated state vector {circumflex over (x)}(t)includes the first and second estimated states {circumflex over (x)}₁(t)and {circumflex over (x)}₂(t) corresponding to the load utilizationprobability estimate {circumflex over (p)}_(load)(t) and theinterference-and-noise sum estimate {circumflex over(P)}_(other)(t)+{circumflex over (P)}_(N)(t).

In addition to the load utilization probability p_(load)(t) and theinterference-and-noise sum P_(other)(t)+P_(N)(t) modeled as first andsecond states x₁(t)=p_(load)(t), x₂(t)=P_(other)(t)+P_(N)(t) in step1310, third and fourth states x₃(t)=Δ L _(own)(t), x₄(t)=x₁(t−T) mayalso be modeled in the state vector x(t) of the state space model instep 1315. The third state x₃(t)=Δ L _(own)(t) can represent a loadfactor bias expressing an error of a scheduled load factor L_(own)(t),and the fourth state x₄(t)=x₁(t−T) can reflect that the load utilizationprobability measurement is subject to a delay corresponding to thesampling period T. The step 1315 need not be performed if the third andfourth states are not used, and therefore, can be considered asoptional. However, the third and fourth states are preferred to be used.

FIG. 14 illustrates a flow chart of an example process performed by theradio network node 410 to implement the step 1340 to update predictedstate vector {circumflex over (x)}(t₁|t₀) when the third and fourthstates are also modeled. In step 1410, the measured total wideband powery_(RTWP)(t₁) applicable at the time t=t₁ can be modeled as:

$\begin{matrix}{{y_{RTWP}\left( t_{1} \right)} = {\frac{x_{2}\left( t_{1} \right)}{1 - {{L_{own}\left( {t_{1} - T_{D}} \right)}{x_{1}\left( t_{1} \right)}} + {x_{3}\left( t_{1} \right)}} + {{e_{RTWP}\left( t_{1} \right)}.}}} & (42)\end{matrix}$

Here, T_(D) can represent a delay between calculation of the scheduleand a time the schedule takes effect on an air interface. Also,e_(RTWP)(t) can represent a measurement error.

In step 1420, the load utilization y_(loadUtilization)(t₁) applicable atthe time t=t₁ as can be modeled as:

y _(loadUtilization)(t ₁)=x ₄(t ₁)+e _(loadUtilization)(t ₁).  (43)

Again, e_(loadUtilization)(t) can represent a measurement error.

In step 1430, a measurement matrix C(t₁) around the predicted statevector {circumflex over (x)}(t₁|t₀) can be obtained. Here, the predictedstate vector {circumflex over (x)}(t₁|t₀) can include the first, second,third, and fourth predicted states {circumflex over (x)}₁(t₁|t₀),{circumflex over (x)}₂(t₁|t₀), {circumflex over (x)}₃(t₁|t₀),{circumflex over (x)}₄(t₁|t₀) which are predicted based on data up tothe time t=t₀. In an embodiment, the measurement matrix C(t₁) can beobtained by determining the measurement matrix C(t₁) linearized aroundthe predicted state vector {circumflex over (x)}(t₁|t₀) such that

${C(t)} = {\frac{\partial{c(x)}}{\partial x}_{x = {\hat{x}{({{t\; 1}{t\; 0}})}}}.}$

In step 1440, a Kalman gain matrix K_(f)(t₁) can be obtained based on atleast the measurement matrix C(t₁), the measurement error vector e(t₁),and a predicted covariance matrix P(t₁|t₀) corresponding to thepredicted state vector {circumflex over (x)}(t₁|t₀). In an embodiment,the Kalman gain matrix K_(f)(t₁) can be obtained by determining:

K _(f)(t ₁)=P(t ₁ |t ₀)C ^(T)(t ₁)(C(t ₁)P(t ₁ |t ₀)C ^(T)(t ₁)+R ₂(t₁))⁻¹  (44)

in which C^(T)(t) is a transpose of the measurement matrix C(t) and(R₂(t)) is a measurement covariance matrix corresponding to themeasurement error vector e(t).

In step 1450, the predicted state vector {circumflex over (x)}(t₁|t₀)can be updated based on at least the Kalman gain matrix K_(f)(t₁), theoutput measurement vector y(t₁), and the measurement vector c(x(t₁)) toobtain the estimated state vector {circumflex over(x)}(t₁|t₁)={circumflex over (x)}(t₁). The estimated state vector{circumflex over (x)}(t₁) can include the first, second, third, andfourth estimated states {circumflex over (x)}₁(t₁), {circumflex over(x)}₂(t₁), {circumflex over (x)}₃(t₁), {circumflex over (x)}₄(t₁). In anembodiment, the estimated state vector {circumflex over(x)}(t₁|t₁)={circumflex over (x)}(t₁) can be obtained throughdetermining:

{circumflex over (x)}(t ₁ |t ₁)={circumflex over (x)}(t ₁ |t ₀)+K _(f)(t₁)(y(t ₁)−c({circumflex over (x)}(t ₁ |t ₀))).  (45)

Here y(t₁) is the measurement vector, with components being the receivedtotal wideband power measurement and the load utilization measurement.

In step 1460, the predicted covariance matrix P(t₁|t₀) can be updatedbased on at least the Kalman gain matrix K_(f)(t₁) and the measurementmatrix C(t₁) to obtain an updated covariance matrix P(t₁|t₁)corresponding to the estimated state vector {circumflex over (x)}(t₁).In an embodiment, the updated covariance matrix P(t₁|t₁) can be obtainedthrough determining:

P(t ₁ |t ₁)=P(t ₁ |t ₀)−K _(f)(t ₁)C(t ₁)P(t ₁ |t ₀).  (46)

Referring back to FIG. 13, when there are first through fourth states,the step 1360 of projecting the estimated state vector {circumflex over(x)}(t₁) can comprise projecting the estimated state vector {circumflexover (x)}(t₁) based on at least the system matrix A(t₁) to obtain thepredicted state vector {circumflex over (x)}(t₂|t₁). Here, the predictedstate vector {circumflex over (x)}(t₂|t₁) includes the first, second,third, and fourth predicted states {circumflex over (x)}₁(t₂|t₁),{circumflex over (x)}₂(t₂|t₁), {circumflex over (x)}₃(t₂|t₁),{circumflex over (x)}₄(t₂|t₁). Then in step 1370, the updated covariancematrix P(t₁|t₁) can be projected to obtain a predicted covariance matrixP(t₂|t₁) based on at least the system matrix A(t₁) and a system noisecovariance matrix R₁(t₁). Back in step 1360, the predicted state vector{circumflex over (x)}(t₂|t₁) can be obtained by determining {circumflexover (x)}(t₂|t₁)=A{circumflex over (x)}(t₁|t₁)+Bu(t₁), and in step 1370,the predicted covariance matrix P(t₂|t₁) can be obtained throughdetermining P(t₂|t₁)=AP(t₁|t₁)A^(T)+R₁(t₁) in which A^(T) is a transposeof the system matrix A(t). Note that the input gain matrix B(t) can beset to zero.

A non-exhaustive list of advantages of the subject matter of thedisclosed subject matter includes:

-   -   Providing other cell interference with a bandwidth corresponding        to one half of a TTI (one half is due to Nyqvists celebrated        theorem that a signal sampled with a certain rate can only        represent the signal with a bandwidth corresponding to half that        rate—or so called aliasing occurs). Conventional algorithms        typically have bandwidths corresponding to the order of tens of        TTIs.    -   Providing estimates that are significantly more accurate than        conventional algorithms.    -   Providing an extended range to provide useful other cell        interference estimates, up to a total interference level of        about 15 dB mean RoT. Previous algorithms only provide useful        accuracies where the other cell interference power is in a small        band well below the 10 dB mean RoT interference level.

Another advantage is in providing estimates of load utilizationprobability and other cell interference that can enhance the performanceof the scheduler and the overall HetNet interference management. Thiscan lead to (among others):

-   -   Enhancing the performance of the whole mobile broadband cellular        system.    -   Simplifying network interference management by providing other        cell interference levels at central nodes in the radio access        network (RAN) and core network (CN).    -   Enabling self organization network (SON) functionality in        wireless networks (such as WCDMA). Such functionality can be        dependent on knowledge of the interference situations in        different cells.

Although the description above contains many specificities, these shouldnot be construed as limiting the scope of the disclosed subject matterbut as merely providing illustrations of some of the presently preferredembodiments. Therefore, it will be appreciated that the scope of thedisclosed subject matter fully encompasses other embodiments, and thatthe scope is accordingly not to be limited. All structural, andfunctional equivalents to the elements of the above-described preferredembodiment that are known to those of ordinary skill in the art areexpressly incorporated herein by reference and are intended to beencompassed hereby. Moreover, it is not necessary for a device or methodto address each and every problem described herein or sought to besolved by the present technology, for it to be encompassed hereby.

What is claimed is:
 1. A method performed at a radio network nodecorresponding to a cell of interest in a wireless network, the methodcomprising: estimating a load utilization probability p_(load)(t₁) basedat least on a load utilization probability estimate {circumflex over(p)}_(load)(t₀) and an interference-and-noise sum estimate {circumflexover (P)}_(other)(t₀)+{circumflex over (P)}_(N)(t₀) applicable at a timet₀ to obtain a load utilization probability estimate {circumflex over(p)}_(load)(t₁) applicable at a time t₁, wherein t₁−t₀=T>0; estimatingan interference-and-noise sum P_(other)(t₁)+P_(N)(t₁) based at least onthe load utilization probability estimate {circumflex over(p)}_(load)(t₀) and the interference-and-noise sum estimate {circumflexover (P)}_(other)(t₀)+{circumflex over (P)}_(N)(t₀) to obtain aninterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) applicable at the timet₁; and estimating an other cell interference P_(other)(t₁) based atleast on the interference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) and a thermal noiseestimate {circumflex over (P)}_(N)(t₁) to obtain an other cellinterference estimate {circumflex over (P)}_(other)(t₁) applicable atthe time t₁, wherein the load utilization probability p_(load)(t)expresses a relationships between radio resource grants scheduled to oneor more cell terminals and radio resource grants used by the same cellterminals applicable at a time t, each cell terminal being a wirelessterminal in the cell of interest, and the load utilization probabilityestimate {circumflex over (p)}_(load)(t) being an estimate thereof,wherein the interference-and-noise sum P_(other)(t)+P_(N)(t) expresses asum of undesired signals, other than an own cell load P_(own)(t),applicable at the time t, and the interference-and-noise sum estimate{circumflex over (P)}_(other)(t)+{circumflex over (P)}_(N)(t) being anestimate thereof, wherein the own cell load P_(own)(t) expresses a sumof signals due to wireless activities in the cell of interest applicableat the time t, wherein the other cell interference P_(other)(t)expresses a sum of interferences present in the cell of interest due towireless activities applicable at the time t in one or more cells otherthan in the cell of interest, and the other cell interference estimate{circumflex over (P)}_(other)(t) being an estimate thereof, and whereina thermal noise P_(N)(t) expresses a sum of undesired signals present inthe cell of interest at the time t other than the own cell loadP_(own)(t) and other than the other cell interference P_(other)(t) andthe thermal noise estimate {circumflex over (P)}_(N)(t) being anestimate thereof.
 2. The method of claim 1, wherein T is equal to atransmission time interval (TTI).
 3. The method of claim 1, wherein thesteps of estimating the load utilization probability p_(load)(t₁) andestimating the interference-and-noise sum P_(other)(t₁)+P_(N)(t₁)comprise: calculating a scheduled load factor L_(own)(t₁−T_(D)); Inobtaining a used load factor L _(own) (t₁−T_(D)); measuring a loadutilization$\frac{{\overset{\_}{L}}_{own}\left( {t_{1} - T_{D}} \right)}{L_{own}\left( {t_{1} - T_{D}} \right)};$obtaining the load utilization probability estimate {circumflex over(p)}_(load)(t₁) based on the measured load utilization$\frac{{\overset{\_}{L}}_{own}\left( {t_{1} - T_{D}} \right)}{L_{own}\left( {t_{1} - T_{D}} \right)};$and obtaining the interference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) based on the measuredload utilization$\frac{{\overset{\_}{L}}_{own}\left( {t_{1} - T_{D}} \right)}{L_{own}\left( {t_{1} - T_{D}} \right)},$wherein the scheduled load factor L_(own)(t−T_(D)) expresses an amountof the radio resource grants scheduled to be used by the cell terminalsfor uplink transmissions at the time t, and wherein the used load factorL _(own)(t−T_(D)) expresses an amount of the scheduled radio resourcegrants used by the cell terminals for the uplink transmissions at thetime t, and wherein T_(D) represents a delay between calculation of thescheduled load factor and a time the schedule takes effect on an airinterface.
 4. The method of claim 1, wherein the steps of estimating theload utilization probability p_(load)(t₁) and estimating theinterference-and-noise sum P_(other)(t₁)+P_(N)(t₁) comprise: measuring atotal wideband power y_(RTWP)(t₁); obtaining the load utilizationprobability estimate {circumflex over (p)}_(load)(t₁) based on themeasured total wideband power y_(RTWP)(t₁); and obtaining theinterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) based on the measuredtotal wideband power y_(RTWP)(t₁), wherein the step of obtaining theinterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) comprises: determining again factor g(t₁) based on the load utilization probability estimate{circumflex over (p)}_(load)(t₁) and the scheduled load factorL_(own)(t₀); modeling the measured total wideband power y_(RTWP)(t₁) asa combination of the interference-and-noise sum estimate {circumflexover (P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) weighted by the gainfactor g(t₁) and a measurement uncertainty e_(RTWP)(t₁); and obtainingthe interference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) based on the measuredtotal wideband power y_(RTWP)(t₁) and the model thereof.
 5. The methodof claim 4, wherein the step of determining the gain factor g(t₁)comprises: determining a load factor bias ΔL_(own)(t₁); and determiningthe gain factor g(t₁) based on the based at least on the loadutilization probability estimate {circumflex over (p)}_(load)(t₁), thescheduled load factor L_(own)(t₀), and the load factor biasΔL_(own)(t₁), wherein the load factor bias ΔL_(own)(t) expresses anerror of the scheduled load factor L_(own)(t).
 6. The method of claim 4,wherein the step of estimating the other cell interference P_(other)(t₁)comprises: obtaining a thermal noise floor estimate {circumflex over(N)}(t₁) corresponding to the cell of interest as the thermal noiseestimate {circumflex over (P)}_(N)(t₁); and subtracting the thermalnoise estimate {circumflex over (P)}_(N) (to from theinterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) to obtain the other cellinterference estimate {circumflex over (P)}_(other)(t₁).
 7. The methodof claim 1, wherein the steps of estimating the load utilizationprobability p_(load)(t₁) and estimating the interference-and-noise sumP_(other)(t₁)+P_(N)(t₁) comprise: modeling the load utilizationprobability p_(load)(t) and the interference-and-noise sumP_(other)(t)+P_(N)(t) as first and second states x₁(t)=p_(load)(t),x₂(t)=P_(other)(t)+P_(N)(t) in a state vector x(t) of a state spacemodel; modeling a measured total wideband power y_(RTWP)(t) and ameasured load utilization y_(loadUtilization)(t) in an outputmeasurement vector y(t) of the state space model; obtaining a predictedstate vector {circumflex over (x)}(t₁|t₀) which includes therein firstand second predicted states {circumflex over (x)}₁(t₁|t₀), {circumflexover (x)}₂(t₁|t₀) whose values are based on the load utilizationprobability estimate {circumflex over (p)}_(load)(t₀) and theinterference-and-noise sum estimate {circumflex over(P)}_(other)(t₀)+{circumflex over (P)}_(N)(t₀); updating the predictedstate vector {circumflex over (x)}(t₁|t₀) based on one or moremeasurements included in an output measurement vector y(t₁) applicableat the time t₁ to obtain an estimated state vector {circumflex over(x)}(t₁|t₁)={circumflex over (x)}(t₁); and obtaining first and secondestimated states {circumflex over (x)}₁(t₁), {circumflex over (x)}₂(t₁)from the estimated state vector {circumflex over (x)}(t₁) respectivelyas the load utilization probability estimate {circumflex over(x)}₁(t₁)={circumflex over (p)}_(load)(t₁) and theinterference-and-noise sum estimate {circumflex over(x)}₂(t₁)={circumflex over (P)}_(other)(t₁)+{circumflex over(P)}_(N)(t₁), wherein modeling errors and measurement errors areincorporated in the state space model as a model error vector w(t) and ameasurement error vector e(t), wherein the predicted state vector{circumflex over (x)}(t|t−T) denotes a prediction of the state vectorx(t) based on information available up to a time t−T, and wherein theestimated state vector {circumflex over (x)}(t|t)={circumflex over(x)}(t) denotes an estimate of the state vector x(t) based oninformation available up to the time t.
 8. The method of claim 7,further comprising: projecting the estimated state vector {circumflexover (x)}(t₁) based at least on dynamic modes corresponding to the cellof interest to obtain a predicted state vector {circumflex over(x)}(t₂|t₁), t₂−t₁=T, wherein the predicted state vector {circumflexover (x)}(t₂|t₁) includes first and second predicted states {circumflexover (x)}₁(t₂|t₁) and {circumflex over (x)}₂(t₂|t₁) whose values arebased on the load utilization probability estimate {circumflex over(p)}_(load)(t₁) and the interference-and-noise sum estimate {circumflexover (x)}₂(t₁)={circumflex over (P)}_(other)(t₁)+{circumflex over(P)}_(N)(t₁), and wherein the state space model is characterized throughequations x(t+T)=A(t)x(t)+B(t)u(t)+w(t) and y(t)=c(x(t))+e(t), in whichx(t) represents the state vector, u(t) represents an input vector, y(t)represents the output measurement vector, w(t) represents the modelerror vector, e(t) represents the measurement error vector, A(t)represents a system matrix describing dynamic modes of the system, B(t)represents an input gain matrix, c(x(t)) represents a measurement vectorwhich is a function of the states of the system, t represents the timeand T represents a sampling period,
 9. The method of claim 8, furthercomprising: modeling third and fourth states x₃(t)=Δ L _(own)(t),x₄(t)=x₁(t−T) in the state vector x(t) of the state space model, thethird state x₃(t)=Δ L _(own)(t) being a load factor bias expressing anerror of a scheduled load factor L_(own)(t), and the fourth statex₄(t)=x₁(t−T) reflecting that the load utilization probabilitymeasurement is subject to a delay corresponding to the sampling periodT, wherein the step of updating the predicted state vector is{circumflex over (x)}(t₁|t₀) comprises: modeling the measured totalwideband power y_(RTWP)(t₁) applicable at the time t₁ as${{y_{RTWP}\left( t_{1} \right)} = {\frac{x_{2}\left( t_{1} \right)}{1 - {{L_{own}\left( {t_{1} - T_{D}} \right)}{x_{1}\left( t_{1} \right)}} + {x_{3}\left( t_{1} \right)}} + {e_{RTWP}\left( t_{1} \right)}}},$T_(D) representing a delay between calculation of the schedule and atime the schedule takes effect on an air interface; modeling the loadutilization y_(loadUtilization)(t₁) applicable at the time t₁ asY_(loadUtilization)(t₁)=x₄ (t₁)+e_(loadUtilization)(t₁); obtaining ameasurement matrix C(t₁) around the predicted state vector {circumflexover (x)}(t₁|t₀), the predicted state vector {circumflex over(x)}(t₁|t_(o)) including first, second, third, and fourth predictedstates {circumflex over (x)}₁(t₁|t₀), {circumflex over (x)}₂(t₁|t₀),{circumflex over (x)}₃(t₁|t₀), {circumflex over (x)}₄(t₁|t₀) predictedbased on data upto the time t₀; obtaining a Kalman gain matrix K_(f)(t₁)based on at least the measurement matrix C(t₁), the measurement errorvector e(t₁), and a predicted covariance matrix P(t₁|t₀) correspondingto the predicted state vector {circumflex over (x)}(t₁|t₀); and updatingthe predicted state vector {circumflex over (x)}(t₁|t₀) based on atleast the Kalman gain matrix K_(f)(t₁), the output measurement vectory(t₁), and the measurement vector c(x(t₁)) to obtain the estimated statevector {circumflex over (x)}(t₁|t₁)={circumflex over (x)}(t₁), theestimated state vector {circumflex over (x)}(t₁) including the first,second, third, and fourth estimated states {circumflex over (x)}₁(t₁),{circumflex over (x)}₂(t₁), {circumflex over (x)}₃(t₁), {circumflex over(x)}₄(t₁); and updating the predicted covariance matrix P(t₁|t₀) basedon at least the Kalman gain matrix K_(f)(t₁) and the measurement matrixC(t₁) to obtain an updated covariance matrix P(t₁|t₁) corresponding tothe estimated state vector {circumflex over (x)}(t₁).
 10. The method ofclaim 9, wherein the step of obtaining the measurement matrix C(t₁)comprises determining the measurement matrix C(t₁) linearized around thepredicted state vector {circumflex over (x)}(t₁|t₀) such that${{C(t)} = {\frac{\partial{c(x)}}{\partial x}_{x = {\hat{x}{({{t\; 1}{t\; 0}})}}}}},$wherein the step of obtaining the Kalman gain matrix K_(f)(t₁) comprisesdetermining K_(f)(t₁)=P(t₁|t₀)C^(T)(t₁)(C(t₁)P(t₁|t₀)C^(T)(t₁)+R₂(t₁))⁻¹in which C^(T)(t) is a transpose of the measurement matrix C(t) and(R₂(t)) is a measurement covariance matrix corresponding to themeasurement error vector e(t), wherein the step of updating thepredicted state vector is {circumflex over (x)}(t₁|t₀) to obtain theestimated state vector {circumflex over (x)}(t₁|t₁)={circumflex over(x)}(t₁) comprises determining {circumflex over (x)}(t₁|t₁)={circumflexover (x)}(t₁|t₀)+K_(f)(t₁)(y(t₁)−c({circumflex over (x)}(t₁|t₀))), andwherein the step of updating the predicted covariance matrix P(t₁|t₀) toobtain the updated covariance matrix P(t₁|t₁) comprises determiningP(t₁|t₁)=P(t₁|t₀)−K_(f)(t₁)C(t₁)P(t₁|t₀).
 11. The method of claim 8,wherein the step of projecting the estimated state vector {circumflexover (x)}(t₁) comprises projecting the estimated state vector{circumflex over (x)}(t₁) based on at least the system matrix A(t₁) toobtain the predicted state vector {circumflex over (x)}(t₂|t₁) whichincludes the first, second, third, and fourth predicted states{circumflex over (x)}₁(t₂|t₁), {circumflex over (x)}₂(t₂|t₁),{circumflex over (x)}₃(t₂|t₁), {circumflex over (x)}₄(t₂|t₁), andwherein the method further comprises projecting the updated covariancematrix P(t₁|t₁) to obtain a predicted covariance matrix P(t₂|t₁) basedon at least the system matrix A(t₁) and a system noise covariance matrixR₁(t₁).
 12. The method of claim 11, wherein the step of projecting theestimated state vector {circumflex over (x)}(t₁) to obtain the predictedstate vector {circumflex over (x)}(t₂|t₁) comprises determining{circumflex over (x)}(t₂|t₁)=A{circumflex over (x)}(t₁|t₁)+Bu(t₁), andwherein the step of projecting the updated covariance matrix P(t₁|t₁) toobtain the predicted covariance matrix P(t₂|t₁) comprises determiningP(t₂|t₁)=AP(t₁|t₁)A^(T)+R₁(t₁) in which A^(T) is a transpose of thesystem matrix A(t).
 13. A radio network node of a wireless network, theradio network node corresponding to a cell of interest and beingstructured to determine an other cell interference P_(other)(t), theradio network node comprising: a transceiver structured to transmit andreceive wireless signals via one or more antennas from and to one ormore cell terminals located within the cell of interest; a communicatorstructured to communicate with other network nodes; and a schedulerstructured to schedule uplink transmissions from the cell terminals,wherein the scheduler is structured to: estimate a load utilizationprobability p_(load)(t₁) based at least on load utilization probabilityestimate {circumflex over (p)}_(load)(t_(O)) and aninterference-and-noise sum estimate {circumflex over(P)}_(other)(t₀)+{circumflex over (P)}_(N)(t₀) applicable at a time t₀to obtain a load utilization probability estimate {circumflex over(p)}_(load)(t₁) applicable at a time t₁, wherein t₁−t₀=T>0, estimate aninterference-and-noise sum P_(other)(t₁)+P_(N)(t₁) based at least on theload utilization probability estimate {circumflex over (p)}_(load)(t₀)and the interference-and-noise sum estimate {circumflex over(P)}_(other)(t₀)+{circumflex over (P)}_(N)(t₀) to obtain aninterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) applicable at the timet₁, and estimate an other cell interference P_(other)(t₁) based at leaston the interference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) and a thermal noiseestimate {circumflex over (P)}_(N)(t₁) to obtain an other cellinterference estimate {circumflex over (P)}_(other)(t₁) applicable atthe time t₁, wherein the load utilization probability p_(load)(t)expresses a relationships between radio resource grants scheduled to theone or more cell terminals and radio resource grants used by the samecell terminals applicable at a time t, each cell terminal being awireless terminal in the cell of interest, and the load utilizationprobability estimate {circumflex over (p)}_(load)(t) being an estimatethereof, wherein the interference-and-noise sum P_(other)(t)+P_(N)(t)expresses a sum of undesired signals, other than an own cell loadP_(own)(t), applicable at the time t, and the interference-and-noise sumestimate {circumflex over (P)}_(other)(t)+{circumflex over (P)}_(N)(t)being an estimate thereof, wherein the own cell load P_(own)(t)expresses a sum of signals due to wireless activities in the cell ofinterest applicable at the time t, wherein the other cell interferenceP_(other)(t) expresses a sum of interferences present in the cell ofinterest due to wireless activities applicable at the time t in one ormore cells other than in the cell of interest, and the other cellinterference estimate {circumflex over (P)}_(other)(t) being an estimatethereof, and wherein a thermal noise P_(N)(t) expresses a sum ofundesired signals present in the cell of interest at the time t otherthan the own cell load P_(own)(t) and other than the other cellinterference P_(other)(t) and the thermal noise estimate {circumflexover (P)}_(N)(t) being an estimate thereof.
 14. The radio network nodeof claim 13, wherein T is equal to a transmission time interval (TTI).15. The radio network node of claim 13, wherein the scheduler isstructured to: calculate a scheduled load factor L_(own)(t₁−T_(D)),obtain a used load factor L _(own)(t₁−T_(D)), measure a load utilization$\frac{{\overset{\_}{L}}_{own}\left( {t_{1} - T_{D}} \right)}{L_{own}\left( {t_{1} - T_{D}} \right)},$and obtain the load utilization probability estimate {circumflex over(p)}_(load)(t₁) and the interference-and-noise sum estimate {circumflexover (P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) based on themeasured load utilization$\frac{{\overset{\_}{L}}_{own}\left( {t_{1} - T_{D}} \right)}{L_{own}\left( {t_{1} - T_{D}} \right)},$wherein the scheduled load factor L_(own)(t−T_(D)) expresses an amountof the radio resource grants scheduled to be used by the cell terminalsfor uplink transmissions at the time t, and wherein the used load factorL _(own)(t−T_(D)) expresses an amount of the scheduled radio resourcegrants used by the cell terminals for the uplink transmissions at thetime t, and wherein T_(D) represents a delay between calculation of thescheduled load factor and a time the schedule takes effect on an airinterface.
 16. The radio network node of claim 15, wherein the scheduleris structured to: measure a total wideband power y_(RTWP)(t), obtain theload utilization probability estimate {circumflex over (p)}_(load)(t₁)based on the measured total wideband power y_(RTWP)(t), determine a gainfactor based on the load utilization probability estimate {circumflexover (p)}_(load)(t₁) and the scheduled load factor L_(own)(t₀), modelthe measured total wideband power y_(RTWP)(t₁) as a combination of theinterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) weighted by the gainfactor and a measurement uncertainty e_(RTWP)(t₁), and obtain theinterference-and-noise sum estimate {circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁) based on the measuredtotal wideband power y_(RTWP)(t₁) and the model thereof.
 17. The radionetwork node of claim 16, wherein the scheduler is structured to:determine a load factor bias ΔL_(own)(t₁), and determine the gain factorbased on the based at least on the load utilization probability estimate{circumflex over (p)}_(load)(t₁), the scheduled load factor L_(own)(t₀),and the load factor bias ΔL_(own)(t₁), wherein the load factor biasΔL_(own)(t₁) expresses an error of the scheduled load factor L_(own)(t).18. The radio network node of claim 16, wherein the scheduler isstructured to: obtain a thermal noise floor estimate {circumflex over(N)}(t₁) corresponding to the cell of interest as the thermal noiseestimate {circumflex over (P)}_(N)(t₁), and subtract the thermal noiseestimate {circumflex over (P)}_(N)(t₁) from the interference-and-noisesum estimate {circumflex over (P)}_(other)(t₁)+{circumflex over(P)}_(N)(t₁).
 19. The radio network node of claim 13, wherein thescheduler is structured to: model the load utilization probabilityp_(load)(t) and the interference-and-noise sum P_(other)(t)+P_(N)(t) asfirst and second states x₁(t)=p_(load)(t), x₂(t)=P_(other)(t)+P_(N)(t)in a state vector x(t) of a state space model, model a measured totalwideband power y_(RTWP)(t) and a measured load utilizationy_(loadUtilization)(t) in an output measurement vector y(t) of the statespace model, obtain a predicted state vector {circumflex over(x)}(t₁|t₀) which includes therein first and second predicted states{circumflex over (x)}₁(t₁|t₀), {circumflex over (x)}₂(t₁|t₀) whosevalues are based on the load utilization probability estimate{circumflex over (p)}_(load)(t₀) and the interference-and-noise sumestimate {circumflex over (P)}_(other)(t₀)+{circumflex over(P)}_(N)(t₀), update the predicted state vector {circumflex over(x)}(t₁|t₀) based on one or more measurements included in an outputmeasurement vector y(t₁) applicable at the time t₁ to obtain anestimated state vector {circumflex over (x)}(t₁|t₁)={circumflex over(x)}(t₁), and obtain first and second estimated states {circumflex over(x)}₁(t₁), {circumflex over (x)}₂(t₁) from the estimated state vector{circumflex over (x)}(t₁) respectively as the load utilizationprobability estimate {circumflex over (x)}₁(t₁)={circumflex over(p)}_(load)(t₁) and the interference-and-noise sum estimate {circumflexover (x)}₂(t₁)={circumflex over (P)}_(other)(t₁)+{circumflex over(P)}_(N)(t₁), wherein modeling errors and measurement errors areincorporated in the state space model as a model error vector w(t) and ameasurement error vector e(t), wherein the predicted state vector{circumflex over (x)}(t|t−T) denotes a prediction of the state vectorx(t) based on information available up to a time t−T, and whereinestimated state vector {circumflex over (x)}(t|t)={circumflex over(x)}(t) denotes an estimate of the state vector x(t) based oninformation available up to the time t.
 20. The radio network node ofclaim 19, wherein the scheduler is structured to: project the estimatedstate vector {circumflex over (x)}(t₁) based at least on dynamic modescorresponding to the cell of interest to obtain a predicted state vector{circumflex over (x)}(t₂|t₁), t₂−t₁=T, wherein the predicted statevector {circumflex over (x)}(t₂|t₁) includes first and second predictedstates {circumflex over (x)}₁(t₂|t₁) and {circumflex over (x)}₂(t₂|t₁)whose values are based on the load utilization probability estimate{circumflex over (p)}_(load)(t₁) and the interference-and-noise sumestimate {circumflex over (x)}₂(t₁)={circumflex over(P)}_(other)(t₁)+{circumflex over (P)}_(N)(t₁), and wherein the statespace model is characterized through equationsx(t+T)=A(t)x(t)+B(t)u(t)+w(t) and y(t)=c(x(t))+e(t). in which x(t)represents the state vector, u(t) represents an input vector, y(t)represents the output measurement vector, w(t) represents the modelerror vector, e(t) represents the measurement error vector, A(t)represents a system matrix describing dynamic modes of the system, B(t)represents an input gain matrix, c(x(t)) represents a measurement vectorwhich is a function of the states of the system, t represents the timeand T represents a sampling period.
 21. The radio network node of claim20, wherein the scheduler is structured to model third and fourth statesx₃(t)=Δ L _(own)(t), x₄(t)=x₁(t−T) in the state vector x(t) of the statespace model, the third state x₃(t)=Δ L _(own)(t) being a load factorbias expressing an error of the scheduled load factor L_(own)(t), andthe fourth state x₄(t)=x₁(t−T) reflecting that the load utilizationprobability measurement is subject to a delay corresponding to thesampling period T, wherein the scheduler is structured to update thepredicted state vector {circumflex over (x)}(t₁|t₀) through: modelingthe measured total wideband power y_(RTWP)(t₁) applicable at the time t₁as${{y_{RTWP}\left( t_{1} \right)} = {\frac{x_{2}\left( t_{1} \right)}{1 - {{L_{own}\left( {t_{1} - T_{D}} \right)}{x_{1}\left( t_{1} \right)}} + {x_{3}\left( t_{1} \right)}} + {e_{RTWP}\left( t_{1} \right)}}},$T_(D) representing a delay between calculation of the schedule and atime the schedule takes effect on an air interface, modeling the loadutilization y_(loadUtilization)(t₁) applicable at the time t₁ asy_(loadUtilization)(t₁)=x₄(t₁)+e_(loadUtilization)(t₁), obtaining ameasurement matrix C(t₁) around the predicted state vector {circumflexover (x)}(t₁|t₀), the predicted state vector {circumflex over(x)}(t₁|t₀) including first, second, third, and fourth predicted states{circumflex over (x)}₁(t₁|t₀), x₂(t₁|t₀), {circumflex over (x)}₃(t₁|t₀),{circumflex over (x)}₄(t₁|t₀) predicted based on data upto the time t₀,obtaining a Kalman gain matrix K_(f)(t₁) based on at least themeasurement matrix C(t₁), the measurement error vector e(t₁), and apredicted covariance matrix P(t₁|t₀) corresponding to the predictedstate vector {circumflex over (x)}(t₁|t₀), updating the predicted statevector {circumflex over (x)}(t₁|t₀) based on at least the Kalman gainmatrix K_(f)(t₁), the output measurement vector y(t₁), and themeasurement vector c(x(t₁)) to obtain the estimated state vector{circumflex over (x)}(t₁|t₁)={circumflex over (x)}(t₁), the estimatedstate vector {circumflex over (x)}(t₁) including the first, second,third, and fourth estimated states {circumflex over (x)}₁(t₁),{circumflex over (x)}₂(t₁), {circumflex over (x)}₃(t₁), {circumflex over(x)}₄(t₁), and updating he predicted covariance matrix P(t₁|t₀) based onat least the Kalman gain matrix K_(f)(t₁) and the measurement matrixC(t₁) to obtain an updated covariance matrix P(t₁|t₁) corresponding tothe estimated state vector {circumflex over (x)}(t₁).
 22. The radionetwork node of claim 21, wherein the scheduler is structured to:determine the measurement matrix C(t₁) linearized around the predictedstate vector {circumflex over (x)}(t₁|t₀) such that${C(t)} = {\frac{\partial{c(x)}}{\partial x}_{x = {\hat{x}{({{t\; 1}{t\; 0}})}}}}$to obtain the measurement matrix C(t₁) comprises determineK_(f)(t₁)=P(t₁|t₀)C^(T)(t₁)(C (t₁)P(t₁|t₀)C^(T)(t₁)+R₂(t₁))⁻¹ in whichC^(T)(t) is a transpose of the measurement matrix C(t) and (R₂(t)) is ameasurement covariance matrix corresponding to the measurement errorvector e(t) to obtain the Kalman gain matrix K_(f)(t₁), determine{circumflex over (x)}(t₁|t₁)={circumflex over(x)}(t₁|t₀)+K_(f)(t₁)(y(t₁)−c({circumflex over (x)}(t₁|t_(O)))) toupdate the predicted state vector {circumflex over (x)}(t₁|t₀) to obtainthe estimated state vector {circumflex over (x)}(t₁|t₁)={circumflex over(x)}(t₁), and determine P(t₁|t₁)=P(t₁|t₀)−K_(f)(t₁)C(t₁)P(t₁|t₀) toupdate the predicted covariance matrix P(t₁|t₀) to obtain the updatedcovariance matrix P(t₁|t₁).
 23. The radio network node of claim 20,wherein the scheduler is structured to: project the estimated statevector {circumflex over (x)}(t₁) based on at least the system matrixA(t₁) to obtain the predicted state vector {circumflex over (x)}(t₂|t₁)which includes the first, second, third, and fourth predicted states{circumflex over (x)}₁(t₂|t₁), {circumflex over (x)}₂(t₂|t₁),{circumflex over (x)}₃(t₂|t₁), {circumflex over (x)}₄(t₂|t₁) to projectthe estimated state vector {circumflex over (x)}(t₁), and project theupdated covariance matrix P(t₁|t₁) to obtain a predicted covariancematrix P(t₂|t₁) based on at least the system matrix A(t₁) and a systemnoise covariance matrix R₁(t₁).
 24. The radio network node of claim 23,wherein the scheduler is structured to: determine {circumflex over(x)}(t₂|t₁)=A{circumflex over (x)}(t₁|t₁)+Bu(t₁) to project theestimated state vector {circumflex over (x)}(t₁) to obtain the predictedstate vector {circumflex over (x)}(t₂|t₁), and determineP(t₂|t₁)=AP(t₁|t₁)A^(T)+R₁(t₁) in which A^(T) is a transpose of thesystem matrix A(t) to project the updated covariance matrix P(t₁|t₁) toobtain the predicted covariance matrix P(t₂|t₁).
 25. A non-transitorycomputer-readable medium which has stored therein programminginstructions, wherein when a computer executes the programminginstructions, the computer executes a method performed in a radionetwork node of a wireless network for determining other cellinterference applicable, wherein the method is the method of claim 1.